Unsupervised learning does not need any human-annotated labels during training. Seneret et al. [1]} introduced an iterative approach which alternates between discriminative learning of the appearance of sub-activities from visual features and generative modeling of the temporal structure of sub-activities. Kukleva et al. [2]} proposed a combination of temporal encoding (generated using a frame time stamp prediction network) and a Viterbi decoding for consistent frame-to-cluster assignment. Gong et al. in ACL [3]} used only the total count of unique actions that appear in the video set as supervisory signal. They propose a two-step clustering and localization iterative procedure. The clustering step provides noisy pseudo-labels for the localization step, and the localization step provides temporal co-attention models to improve the clustering performance.
